Chongwei Shi Advances Biostatistical Methods and Computational Biology Through Deep Learning and Statistical Shape Analysis

A deep learning framework combines convolutional and bidirectional recurrent networks to improve protein function prediction from genomic ...
How-To Geek on MSN

Generate realistic test data in Python fast. No dataset required

Learn the NumPy trick for generating synthetic data that actually behaves like real data.
GitHub

Arduino library for faster generation of random numbers.

This library contains functions that have the goal to deliver random bits faster than the build in random function can, while still using it. The idea is to have a buffer ( __randomBuffer) which can ...
IEEE

Characterization and Selection of Probability Density Function in a Discrete Random Switching Period SVPWM Strategy

Abstract: Aiming at suppressing the switching frequency harmonics in the integrated permanent magnet synchronous motor (PMSM) systems, a discrete random switching period (DRSP) space vector pulse ...
IEEE

Deep Learning Algorithms for the Work Function Fluctuation of Random Nanosized Metal Grains on Gate-All-Around Silicon Nanowire MOSFETs

Abstract: Device simulation has been explored and industrialized for over 40 years; however, it still requires huge computational cost. Therefore, it can be further advanced using deep learning (DL) ...
GitHub

Python Functions for MemGPT

These functions created for use with MemGPT are designed with the Windows operating system in mind. Mostly the os dependency is file-system related and they can be edited to work on linux and Mac ...